Enhanced phase synchrony in the electroencephalograph gamma band for musicians while listening to music

Music Familiarization Elicits Functional Connectivity Between Right Frontal/Temporal and Parietal Areas in the Theta and Alpha Bands

Frequent listening to unfamiliar music excerpts forms functional connectivity in the brain as music becomes familiar and memorable. However, where these connections spectrally arise in the cerebral cortex during music familiarization has yet to be determined. This study investigates electrophysiological changes in phase-based functional connectivity recorded with electroencephalography (EEG) from twenty participants' brains during thrice passive listening to initially unknown classical music excerpts. Functional connectivity is evaluated based on measuring phase synchronization between all pairwise combinations of EEG electrodes across all repetitions via repeated measures ANOVA and between every two repetitions of listening to unknown music with the weighted phase lag index (WPLI) method in different frequency bands. The results indicate an increased phase synchronization during gradual short-term familiarization between the right frontal and the right parietal areas in the theta and alpha bands. In addition, the increased phase synchronization is discovered between the right temporal areas and the right parietal areas at the theta band during gradual music familiarization. Overall, this study explores the short-term music familiarization effects on neural responses by revealing that repetitions form phasic coupling in the theta and alpha bands in the right hemisphere during passive listening.

Early musical training benefits to non-musical cognitive ability associated with the Gestalt principles

Musical training has been evidenced to facilitate music perception, which refers to the consistencies, boundaries, and segmentations in pieces of music that are associated with the Gestalt principles. The current study aims to test whether musical training is beneficial to non-musical cognitive ability with Gestalt principles. Three groups of Chinese participants (with early, late, and no musical training) were compared in terms of their performances on the Motor-Free Visual Perception Test (MVPT). The results show that the participants with early musical training had significantly better performance in the Gestalt-like Visual Closure subtest than those with late and no musical training, but no significances in other Gestalt-unlike subtests was identified (Visual Memory, Visual Discrimination, Spatial Relationship, Figure Ground in MVPT). This study suggests the benefit of early musical training on non-musical cognitive ability with Gestalt principles.

Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training

In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.

A new neurofeedback training method based on feature space clustering to control EEG features within target clusters

BACKGROUND

Within the most commonly used neurofeedback training methods, a threshold has been defined for each EEG feature wherein subjects' status during training can be assessed according to the given value. In the present study, a neurofeedback training method based on feature-space clustering was proposed in order to assess subjects' status more accurately.

NEW METHOD

Neural gas algorithm was employed for feature space clustering. Then, the clusters were labeled as initial clusters (where the EEG features were placed prior to training) and target (where the EEG features should be shifted towards during training) ones. A scoring index was defined whose value was determined according to subjects' brain activity. This method was simulated in two versions: soft-boundary and hard-boundary based methods.

RESULTS

The results of the present simulation showed that the proposed hard-boundary based version could guide the subjects towards the boundaries of the target clusters and even their status would be stabilized in case of too many changes in subjects' EEG features. In the proposed soft-boundary based version, in case of too many changes in training features, the subjects would not be encouraged and they could be guided towards the target boundaries.

CONCLUSION

The proposed hard-boundary based version could be effective in guiding a subject towards being placed within the boundaries of target clusters and even beyond them if no specific limits exited for EEG features. As well, the soft-boundary based version could be useful when controlling EEG features within a limit.

The neural underpinnings of music listening under different attention conditions

Most studies examining the neural underpinnings of music listening have no specific instruction on how to process the presented musical pieces. In this study, we explicitly manipulated the participants' focus of attention while they listened to the musical pieces. We used an ecologically valid experimental setting by presenting the musical stimuli simultaneously with naturalistic film sequences. In one condition, the participants were instructed to focus their attention on the musical piece (attentive listening), whereas in the second condition, the participants directed their attention to the film sequence (passive listening). We used two instrumental musical pieces: an electronic pop song, which was a major hit at the time of testing, and a classical musical piece. During music presentation, we measured electroencephalographic oscillations and responses from the autonomic nervous system (heart rate and high-frequency heart rate variability). During passive listening to the pop song, we found strong event-related synchronizations in all analyzed frequency bands (theta, lower alpha, upper alpha, lower beta, and upper beta). The neurophysiological responses during attentive listening to the pop song were similar to those of the classical musical piece during both listening conditions. Thus, the focus of attention had a strong influence on the neurophysiological responses to the pop song, but not on the responses to the classical musical piece. The electroencephalographic responses during passive listening to the pop song are interpreted as a neurophysiological and psychological state typically observed when the participants are 'drawn into the music'.

Experimental evidence of deterministic coherence resonance in coupled chaotic systems with frequency mismatch

We present the experimental evidence of deterministic coherence resonance in unidirectionally coupled two and three Rössler electronic oscillators with mismatch between their natural frequencies. The regularity in both the amplitude and the phase of chaotic fluctuations is experimentally proven by the analyses of normalized standard deviations of the peak amplitude and interpeak interval and Lyapunov exponents. The resonant chaos suppression appears when the coupling strength is increased and the oscillators are in phase synchronization. In two coupled oscillators, the coherence enhancement is associated with negative third and fourth Lyapunov exponents, while the largest first and second exponents remain positive. Distinctly, in three oscillators coupled in a ring, all exponents become negative, giving rise to periodicity. Numerical simulations are in good agreement with the experiments.

Studying the default mode and its mindfulness-induced changes using EEG functional connectivity

The default mode network (DMN) has been largely studied by imaging, but not yet by neurodynamics, using electroencephalography (EEG) functional connectivity (FC). mindfulness meditation (MM), a receptive, non-elaborative training is theorized to lower DMN activity. We explored: (i) the usefulness of EEG-FC for investigating the DMN and (ii) the MM-induced EEG-FC effects. To this end, three MM groups were compared with controls, employing EEG-FC (-MPC, mean phase coherence). Our results show that: (i) DMN activity was identified as reduced overall inter-hemispheric gamma MPC during the transition from resting state to a time production task and (ii) MM-induced a state increase in alpha MPC as well as a trait decrease in EEG-FC. The MM-induced EEG-FC decrease was irrespective of expertise or band. Specifically, there was a relative reduction in right theta MPC, and left alpha and gamma MPC. The left gamma MPC was negatively correlated with MM expertise, possibly related to lower internal verbalization. The trait lower gamma MPC supports the notion of MM-induced reduction in DMN activity, related with self-reference and mind-wandering. This report emphasizes the possibility of studying the DMN using EEG-FC as well as the importance of studying meditation in relation to it.

An index of signal mode complexity based on orthogonal transformation

Irregular and complex signals are ubiquitous in nature. The principal aim of this paper is to develop an index, quantifying the complexity of such signals, which is based on the distribution of the strengths of its orthogonal oscillatory modes estimated by singular value decomposition. The index is first tested with simulated chaotic and/or stochastic maps and flows. Among neural data analysis, the index is first applied to a cognitive EEG data set recorded from two groups, musicians and non-musicians, during listening to music and resting state. In the gamma band (30-50 Hz), musicians showed robust changes in complexity, consistent over various scalp regions, during listening to music from resting condition, whereas such changes were minimal for non-musicians. Then the index is used to separate healthy participants from epileptic and manic patients based on spontaneous EEG analysis. Finally, it is used to track a tonic-clonic seizure EEG signal, and a conspicuous change was found in the complexity profiles of delta band (1-3.5 Hz) oscillations at the onset of seizure. We conclude that this index would be useful for quantification of a wide range of time series including neural oscillations.

The application of graph theoretical analysis to complex networks in the brain

Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features.

Brain, music, and non-Poisson renewal processes

In this paper we show that both music composition and brain function, as revealed by the electroencephalogram (EEG) analysis, are renewal non-Poisson processes living in the nonergodic dominion. To reach this important conclusion we process the data with the minimum spanning tree method, so as to detect significant events, thereby building a sequence of times, which is the time series to analyze. Then we show that in both cases, EEG and music composition, these significant events are the signature of a non-Poisson renewal process. This conclusion is reached using a technique of statistical analysis recently developed by our group, the aging experiment (AE). First, we find that in both cases the distances between two consecutive events are described by nonexponential histograms, thereby proving the non-Poisson nature of these processes. The corresponding survival probabilities Psi(t) are well fitted by stretched exponentials [Psi(t) proportional, variant exp (-(gammat){alpha}) , with 0.5<alpha<1 .] The second step rests on the adoption of AE, which shows that these are renewal processes. We show that the stretched exponential, due to its renewal character, is the emerging tip of an iceberg, whose underwater part has slow tails with an inverse power law structure with power index mu=1+alpha. Adopting the AE procedure we find that both EEG and music composition yield mu<2. On the basis of the recently discovered complexity matching effect, according to which a complex system S with mu{S}<2 responds only to a complex driving signal P with mu{P}< or =mu{S}, we conclude that the results of our analysis may explain the influence of music on the human brain.

Experimental evidence of anomalous phase synchronization in two diffusively coupled Chua oscillators

We study the transition to phase synchronization in two diffusively coupled, nonidentical Chua oscillators. In the experiments, depending on the used parameterization, we observe several distinct routes to phase synchronization, including states of either in-phase, out-of-phase, or antiphase synchronization, which may be intersected by an intermediate desynchronization regime with large fluctuations of the frequency difference. Furthermore, we report the first experimental evidence of an anomalous transition to phase synchronization, which is characterized by an initial enlargement of the natural frequency difference with coupling strength. This results in a maximal frequency disorder at intermediate coupling levels, whereas usual phase synchronization via monotonic decrease in frequency difference sets in only for larger coupling values. All experimental results are supported by numerical simulations of two coupled Chua models.

Phase synchronization from noisy univariate signals

We present methods for detecting phase synchronization of two unidirectionally coupled, self-sustained noisy oscillators from a signal of the driven oscillator alone. One method detects soft phase locking; another hard phase locking. Both are applied to the problem of detecting phase synchronization in von Kármán vortex flow meters.

Measuring synchronization in neuronal networks for biosensor applications

Cultures of neurons can be grown on microelectrode arrays (MEAs), so that their spike and burst activity can be monitored. These activity patterns are quite sensitive to changes in the environment, such as chemical exposure, and hence the cultures can be used as biosensors. One key issue in analyzing the data from neuronal networks is how to quantify the level of synchronization among different units, which represent different neurons in the network. In this paper, we propose a synchronization metric, based on the statistical distribution of unit-to-unit correlation coefficients. We show that this synchronization metric changes significantly when the networks are exposed to bicuculline, strychnine, or 2,3-dioxo-6-nitro-l,2,3,4-tetrahydrobenzoquinoxaline-7-sulphonamide (NBQX). For that reason, this metric can be used to characterize pharmacologically induced changes in a network, either for research or for biosensor applications.

Data-driven optimal filtering for phase and frequency of noisy oscillations: Application to vortex flow metering

A method for measuring the phase of oscillations from noisy time series is proposed. To obtain the phase, the signal is filtered in such a way that the filter output has minimal relative variation in the amplitude over all filters with complex-valued impulse response. The argument of the filter output yields the phase. Implementation of the algorithm and interpretation of the result are discussed. We argue that the phase obtained by the proposed method has a low susceptibility to measurement noise and a low rate of artificial phase slips. The method is applied for the detection and classification of mode locking in vortex flow meters. A measure for the strength of mode locking is proposed.

Bivariate surrogate techniques: necessity, strengths, and caveats

The concept of surrogates allows testing results from time series analysis against specified null hypotheses. In application to bivariate model dynamics we here compare different types of surrogates, each designed to test against a different null hypothesis, e.g., an underlying bivariate linear stochastic process. Two measures that aim at a characterization of interdependence between nonlinear deterministic dynamics were used as discriminating statistics. We analyze eight different stochastic and deterministic models not only to demonstrate the power of the surrogates, but also to reveal some pitfalls and limitations.

EEG gamma-band phase synchronization between posterior and frontal cortex during mental rotation in humans

The main purpose of the present paper was: (1) to study the phase synchronization pattern in the gamma-band while performing the classical Shepard-Metzler task of mental rotation; (2) to investigate the role of musical training; and (3) to study hemispheric differences in the degree of synchronization during mental rotation. Multivariate electroencephalograph signals from 20 male subjects (ten musicians and ten non-musicians) were recorded while performing the mental rotation task and also at resting condition. Phase synchronization was measured by a recent index, mean phase coherence. It was found that synchronization between frontal cortex and right parietal cortex was significantly increased during mental rotation with respect to rest, whereby musicians showed significantly higher degrees of synchronization than non-musicians. Left hemispheric dominance in the degree of phase synchronization, stronger in the posterior right parietal and occipital regions, was observed in musicians. Right hemispheric dominance was generally observed in non-musicians.

Long-range synchrony in the gamma band: role in music perception

Synchronization seems to be a central mechanism for neuronal information processing within and between multiple brain areas. Furthermore, synchronization in the gamma band has been shown to play an important role in higher cognitive functions, especially by binding the necessary spatial and temporal information in different cortical areas to build a coherent perception. Specific task-induced (evoked) gamma oscillations have often been taken as an indication of synchrony, but the presence of long-range synchrony cannot be inferred from spectral power in the gamma range. We studied the usefulness of a relatively new measure, called similarity index to detect asymmetric interdependency between two brain regions. Spontaneous EEG from two groups-musicians and non-musicians-were recorded during several states: listening to music, listening to text, and at rest (eyes closed and eyes open). While listening to music, degrees of the gamma band synchrony over distributed cortical areas were found to be significantly higher in musicians than non-musicians. Yet no differences between these two groups were found at resting conditions and while listening to a neutral text. In contrast to the degree of long-range synchrony, spectral power in the gamma band was higher in non-musicians. The degree of spatial synchrony, a measure of signal complexity based on eigen-decomposition method, was also significantly increased in musicians while listening to music. As compared with non-musicians, the finding of increased long-range synchrony in musicians independent of spectral power is interpreted as a manifestation of a more advanced musical memory of musicians in binding together several features of the intrinsic complexity of music in a dynamical way.